GBM Simulation

A lightweight Python library for simulating geometric Brownian motion (GBM) paths and managing parameterized simulation experiments.

This project was created as a learning exercise in object-oriented Python design, API development, packaging, and automated testing. The library demonstrates how to structure a small simulation framework that separates model definitions, simulation configuration, and simulation results.

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Features

• Geometric Brownian Motion model implementation
• Immutable time-series container (Path)
• Parameter configuration objects (ParameterSet)
• Hashable parameter sets suitable for caching simulations
• Simple Monte Carlo path generation
• Basic automated test suite using pytest

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Installation

Clone the repository and install the package in editable mode:

git clone https://github.com/<your-username>/gbm_simulation.git
cd gbm_simulation
pip install -e .

Install dependencies:

pip install -r requirements.txt

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Quick Example

from gbm_simulation import ParameterSet

# Define simulation parameters
params = ParameterSet(mu=0.1, sigma=0.2, T=1.0, steps=100)

# Run simulation
path = params.simulation()

print(len(path))
print(path[0])

Example output:

101
(0.0, 1.0)

For a complete runnable example see: examples/basic_simulation.py

Run it with:

python examples/basic_simulation.py

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Project Structure

gbm_simulation/
│
├─ gbm_simulation/
│   ├─ __init__.py
│   ├─ gbm.py
│   ├─ path.py
│   └─ parameters.py
│
├─ tests/
│   ├─ test_gbm.py
│   ├─ test_path.py
│   └─ test_parameters.py
│
├─ requirements.txt
├─ pyproject.toml
└─ README.md

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Core Components

GBM

Implements a geometric Brownian motion model with:

• drift parameter mu
• volatility parameter sigma
• Monte Carlo path generation

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Path

Immutable container representing a simulated time series.

Features:

• indexing
• slicing
• length queries
• representation suitable for debugging

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ParameterSet

Configuration object representing a full simulation setup:

(mu, sigma, T, steps)

Parameter sets are:

• immutable
• comparable
• hashable

This allows them to be used as keys in dictionaries for simulation caching or experiment tracking.

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Running Tests

Execute the test suite with:

pytest

The tests verify the behavior of:

• GBM
• Path
• ParameterSet

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Dependencies

• numpy

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Purpose of the Project

This repository demonstrates:

• Python package structure
• API design for internal libraries
• object-oriented modeling
• dependency management
• automated testing with pytest

The project is intentionally small and focuses on clear structure and reproducibility rather than